Robot Communication Research Articles

Optimizing Communication for a Humanoid Subs-Talker Robot Using RSSI Analysis on ROS System

This study aims to optimize communication for the humanoid "Subs-Talker" robot using Receive Signal Strength Indicator (RSSI) analysis within the Robot Operating System (ROS). The background of this research highlights the importance of stable and strong inter-robot communication in the Indonesian Humanoid Robot Soccer Contest (KRSBI-H), where robots need to function in synergy. The method involves measuring signal strength at specific distances, both with and without the use of an extender device, utilizing the InSSIDer application for RSSI data recording and analysis. The results show that using an extender improves communication stability, especially at longer distances (7.2 and 9.0 meters), although the impact is minimal at closer distances. The discussion suggests that an extender can be an effective solution for enhancing inter-robot communication quality in competitions. In conclusion, the extender significantly improves the communication range and signal quality among robots, supporting optimal performance in collaborative robot interactions within the competition arena.

New colleague or gimmick hurdle? A user-centric scoping review of the barriers and facilitators of robots in hospitals.

Healthcare systems are confronted with a multitude of challenges, including the imperative to enhance accessibility, efficiency, cost-effectiveness, and the quality of healthcare delivery. These challenges are exacerbated by current healthcare personnel shortages, prospects of future shortfalls, insufficient recruitment efforts, increasing prevalence of chronic diseases, global viral concerns, and ageing populations. To address this escalating demand for healthcare services, healthcare systems are increasingly adopting robotic technology and artificial intelligence (AI), which promise to optimise costs, improve working conditions, and increase the quality of care. This article focuses on deepening our understanding of the barriers and facilitators associated with integrating robotic technologies in hospital environments. To this end, we conducted a scoping literature review to consolidate emerging themes pertaining to the experiences, viewpoints perspectives, and behaviours of hospital employees as professional users of robots in hospitals. Through screening 501 original research articles from Web-of-Science, we identified and reviewed in full-text 40 pertinent user-centric studies of the integration of robots into hospitals. Our review revealed and analysed 14 themes in-depth, of which we identified seven as barriers and seven as facilitators. Through a structuring of the barriers and facilitators, we reveal a notable misalignment between these barriers and facilitators: Finding that organisational aspects are at the core of most barriers, we suggest that future research should investigate the dynamics between hospital employees as professional users and the procedures and workflows of the hospitals as institutions, as well as the ambivalent role of anthropomorphisation of hospital robots, and emerging issues of privacy and confidentiality raised by increasingly communicative robots. Ultimately, this perspective on the integration of robots in hospitals transcends debates on the capabilities and limits of the robotic technology itself, shedding light on the complexity of integrating new technologies into hospital environments and contributing to an understanding of possible futures in healthcare innovation.

Facing LLMs: Robot Communication Styles in Mediating Health Information between Parents and Young Adults

Young adults may feel embarrassed when disclosing sensitive information to their parents, while parents might similarly avoid sharing sensitive aspects of their lives with their children. How to design interactive interventions that are sensitive to the needs of both younger and older family members in mediating sensitive information remains an open question. In this paper, we explore the integration of large language models (LLMs) with social robots. Specifically, we use GPT-4 to adapt different Robot Communication Styles (RCS) for a social robot mediator designed to elicit self-disclosure and mediate health information between parents and young adults living apart. We design and compare four literature-informed RCS: three LLM-adapted (Humorous, Self-deprecating, and Persuasive) and one manually created (Human-scripted), and assess participant perceptions of Likeability, Usefulness, Helpfulness, Relatedness, and Interpersonal Closeness . Through an online experiment with 183 participants, we assess the RCS across two groups: adults with children (Parents) and young adults without children (Young Adults). Our results indicate that both Parents and Young Adults favoured the Human-scripted and Self-deprecating RCS as compared to the other two RCS. The Self-deprecating RCS furthermore led to increased relatedness as compared to the Humorous RCS. Our qualitative findings reveal challenges people have in disclosing health information to family members, and who normally assumes the role of family facilitator-two areas in which social robots can play a key role. The findings offer insights for integrating LLMs with social robots in health-mediation and other contexts involving the sharing of sensitive information.

Teleoperated Communication Robot: A Law Enforcement Perspective

The goal of this paper is to investigate the use of communication robots as tools to increase the safety of first responders (specifically, law enforcement officers) during social interactions or first-response situations. This paper functions as the first stage of our investigation into communication robots for use by first responders. The law enforcement officers (LEOs) in our study were provided with a mobile communication robot to determine if such robots could be of use in the public domain for the benefit of both civilians and LEOs. As part of our study, LEOs participated in training sessions where they worked with non-weaponized teleoperated communication robots and completed pretest-posttest surveys. The surveys captured LEOs’ perceptions and attitudes towards the robot and examined the impact of robot design features on LEOs’ acceptance of the communication robot. This paper discusses the significance and potential use of communication robots as tools in the future of law enforcement. Our future works include conducting additional studies involving affected stakeholders (e.g., citizen communities) and assessing/mitigating any potential negative impact of communication robots.

Research on AUV Multi-Node Networking Communication Based on Underwater Electric Field CSMA/CA Channel

To address the issues of high attenuation, weak reception signal, and channel blockage in the current electric field communication of underwater robots, research on autonomous underwater vehicle (AUV) multi-node networking communication based on underwater electric field Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel was conducted. This article, first through simulation, finds that the Optimized Link State Routing (OLSR) protocol has a smaller routing packet delay time and higher reliability compared to the Ad Hoc On-Demand Distance Vector (AODV) protocol on underwater electric field CSMA/CA channels. Then, a 2FSK underwater electric field communication system was established, and dynamic communication experiments were carried out between two AUV nodes. The experimental results showed that within a range of 0 to 3.5 m, this system can achieve underwater dynamic electric field communication with a bit error rate of 0 to 0.628%. Finally, to avoid channel blockage during underwater AUV multi-node communication, this article proposes a dynamic backoff method for AUV multi-node communication based on CSMA/CA. This system can achieve dynamic multi-node communication of underwater electric fields with an error rate ranging from 0 to 0.96%. The research results have engineering application prospects for underwater cluster operations.

A Highly-Sensitive Omnidirectional Acoustic Sensor for Enhanced Human-Machine Interaction.

Acoustic sensor-based human-machine interaction (HMI) plays a crucial role in natural and efficient communication in intelligent robots. However, accurately identifying and tracking omnidirectional sound sources, especially in noisy environments still remains a notable challenge. Here, a self-powered triboelectric stereo acoustic sensor (SAS) with omnidirectional sound recognition and tracking capabilities by a 3D structure configuration is presented. The SAS incorporates a porous vibrating film with high electron affinity and low Young's modulus, resulting in high sensitivity (3172.9 mVpp Pa-1) and a wide frequency response range (100-20 000 Hz). By utilizing its omnidirectional sound recognition capability and adjustable resonant frequency feature, the SAS can precisely identify the desired audio signal with an average deep learning accuracy of 98%, even in noisy environments. Moreover, the SAS can simultaneously recognize multiple individuals in the auxiliary conference system and the driving commands under background music in self-driving vehicles, which marks a notable advance in voice-based HMI systems.

Multi-objective QoS optimization in swarm robotics

The “Internet of Robotic Things” (IoRT) is a concept that connects sensors and robotic objects. One of the practical applications of IoRT is swarm robotics, where multiple robots collaborate in a shared workspace to accomplish assigned tasks that may be challenging or impossible for a single robot to conquer. Swarm robots are particularly useful in critical situations, such as post-earthquake scenarios, where they can locate survivors and provide assistance in areas inaccessible to humans. In these life-saving situations, reliable and prompt communication among swarm robots is of utmost importance. To address the need for highly dependable and low-latency communication in swarm robotics, this research introduces a novel hybrid approach called Multi-objective QoS optimization based on Support vector regression and Genetic algorithm (MQSG). The MQSG method consists of two main phases: Parameter Relationship Identification and Parameter Optimization. In the Parameter Relationship Identification phase, the relationship between network inputs (Packet inter-arrival time, Packet size, Transmission power, Distance between sender and receiver) and outputs (quality of service (QoS) parameters) is established using support vector regression. In the parameter optimization phase, a multi-objective function is created based on the obtained relationships from the Parameter Relationship Identification phase. By solving this multi-objective function, optimal values for each QoS parameter are determined, leading to enhanced network performance. Simulation results demonstrate that the MQSG method outperforms other similar algorithms in terms of transmission latency, packet delivery rate, and the number of retransmitted packets.

Challenges in family support systems for older adult individuals living alone using communication robots

Challenges in family support systems for older adult individuals living alone using communication robots

The effect of variation in repetitive utterances on users by a chat-oriented communication robot

The effect of variation in repetitive utterances on users by a chat-oriented communication robot

Preventing isolation of people with dementia: Effectiveness and challenges of communication robots

Preventing isolation of people with dementia: Effectiveness and challenges of communication robots

A Multimodal Robotic Communication System for Students with Profound and Multiple Disabilities

Different researches claim that using a robot toplay with students with profound and multiple disabilitiesmight be a valuable tool to increase the learning curve ofthose students. Robots in schools and in other educationalenvironments is a growing research field and it seems that it is time for technology to transform the education withpeople of special needs similarly to how technology affectedmainstream education. The project focuses on usingdifferent switches plugged in a joybox to send signals toNAO Robot. Each switch caries a certain behavior. JoyBox contains eight numbered connectors and four directionconnectors to move the robot. The paper implements clientserver communication to connect to the robot and be able tosend signals to the robot via the switches connected to theJoyBox. Using the switch enables the users to communicate to NAO independently. This gauntness a deep level ofengagement with the robot, since the robot moves based onthe singles received from the switches. The paper describesthe setup of the client server communication, and the robotknows which behaviour to implement next. This study implements a case study evaluated by only four researchersand staff from Oakfield school to evaluate the software .Thisstudy hopes to be able to assess the needs of school staff toimprove communicational methods for students withprofound and multiple disabilities.

Collaborative gas source localization strategy with networked nano-drones in unknown cluttered environments

This paper introduces a novel approach for improving gas source localization in dynamic urban environments, employing a swarm of nano-Crazyflie drones through a hybrid strategy that integrates Adaptive Robotic Particle Swarm Optimization (ARPSO) with Bidirectional Brain Emotional Learning (BBEL). The proposed method refines the ARPSO algorithm into two phases: an initial exploration phase and a subsequent seeking phase. The exploration phase activates when no gas is detected, seamlessly transitioning to the seeking phase upon gas detection. This facilitates efficient information exchange and desired velocity generation within the particle swarm. During the seeking phase, particles conduct measurements, share the global best position, intelligently navigate obstacles, and avoid collisions while directly identifying the global concentration field maximum. Unlike conventional methods, the ARPSO algorithm autonomously adapts its parameters online, mitigating algorithmic failures and local optima challenges. It also considers the limited communication of robots in complex environments, a factor often overlooked in recent methods. To enhance plume tracking robustness, facilitate the controller gain tuning process, and ensure system resilience against environmental disturbances and uncertainties, the BBEL method incorporates a sophisticated fuzzy neural network structure with reinforcement learning-based adaptation mechanisms. The stability of the closed-loop control system is rigorously proven using Lyapunov theory. Numerical simulations in complex urban scenarios validate the algorithm’s effectiveness, showcasing a significant 20% improvement in turnaround time and a flawless 100% success rate with no collisions, in addition to enhanced capabilities in handling uncertainties and disturbances compared to benchmarks like ARPSO-BBEL with unlimited communication range and no delayed communications (ARPSO-BBELU), Sniffy Bug (SB)-BBELU, and ARPSO-PIDU.

Wireless Communication-aware Path Planning and Multiple Robot Navigation Strategies for Assisted Inspections

Among the many challenges robots encounter in the mining industry, exploring confined environments receives significant attention. This work tackles problems associated with robot communication in hazardous and confined environments, where its cluttered and extensive nature frequently precludes traditional cable-based and wireless solutions. Our methods resort to off-the-shelf long-range radio frequencies to profile the signal propagation behaviour over the geometrical map to assist navigation algorithms that seek to preserve the connection. We consider mathematical models to predict signal power behaviour and serve as input to path planning. We also propose a semi-autonomous leader-follower scheme, with signal repeater units forming a mobile wireless network to enable inspection in hard-to-reach locations. Finally, we present a multi-robot connection-aware system, combining path planning based on radio signal power with multiple robot navigation. Results show the applicability of the proposed solutions, generating single and multi-robot paths for optimal signal reception based on power estimation, thus enabling operations in remote and isolated areas with no line-of-sight between the command base and the robotic inspection device. Experiments conducted in long corridors and in a representative mining environment using the EspeleoRobô and Pioneer platforms demonstrate significant improvements over the traditional communication methods for robotic operation regarding communication quality and inspection range limits.

Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency

Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.

Experimental analysis for comparison of wireless transmission technologies: Wi-Fi, Bluetooth, ZigBee and LoRa for mobile multi-robot in hostile sites

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-to-noise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments.

Exploring law enforcement officers’ expectations and attitudes about communication robots in police work

This study explores the perspectives of law enforcement officers (LEOs) on the implementation of communication robots in police work. Through two approaches - open-ended questionnaires (N = 37) and focus group discussions (N = 14) - the research aims to gather LEOs' opinions and attitudes on the potential future use of these robots. The findings reveal a general skepticism among LEOs, who express low trust in communication robots. Despite this, a majority acknowledge that robots could enhance communication effectiveness in policing. The study highlights the need for increased public education and enhanced performance accuracy of robots to address LEOs' concerns and foster greater acceptance of this technology in law enforcement.

A vision-language-guided robotic action planning approach for ambiguity mitigation in human–robot collaborative manufacturing

Human–robot collaboration (HRC) has been recognized as a potent pathway towards mass personalization in the manufacturing sector, by leveraging the synergy of human creativity and robotic precision. Previous approaches rely heavily on visual perception to autonomously comprehend the HRC environment. However, the inherent ambiguity in human–robot communication cannot be consistently neutralized by relying solely on visual cues. With the recently soaring popularity of large language models (LLMs), the consideration of language data as a complementary information source has increasingly drawn research attention, while the application of such large models, particularly within the context of HRC in manufacturing, remains largely under-explored. In response to this gap, a vision-language reasoning approach is proposed to mitigate the communication ambiguity prevalent in human–robot collaborative manufacturing scenarios. A referred object retrieval model is first designed to alleviate the object–reference ambiguity in the human language command. This model is then seamlessly integrated into an LLM-based robotic action planner to achieve an improved HRC performance. The effectiveness of the proposed approach is demonstrated empirically through a series of experiments conducted on the object retrieval model and its application in a human–robot collaborative assembly case.

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

Enhancing human–robot communication with a comprehensive language-conditioned imitation policy for embodied robots in smart cities

Integrating Embodied Robots into a smart city’s networked system can significantly enhance the city’s operational efficiency. These robots can be connected to the city’s network, receiving and transmitting data in real time for enhancing human–robot communications. Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions. However, most language-conditioned policy-related research is limited to specific datasets and cannot generalize across different environments. In this study, we propose a novel imitation learning framework tailored for language-conditioned robotic tasks. Our framework includes specialized encoders designed for various benchmarks and utilizes two distinct models: the Transformer and Diffusion models. We rigorously evaluate this framework in three different robotic environments. Our findings indicate that the framework consistently delivers superior performance across multiple domains. Notably, we observe that the Transformer model is particularly effective in managing tasks with long trajectories, whereas the Diffusion model demonstrates enhanced proficiency in generating trajectories from limited training datasets. Our approach showcases remarkable generalization capabilities across a range of tasks and achieves significantly higher success rates in task completion.

Research on the Intelligent System Architecture and Control Strategy of Mining Robot Crowds

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.